ArticleBiomedical optics express2018
Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 122 citations in OpenAlex.
- Leveraging CNN and Transfer Learning With EfficientNet for Enhanced Optical Coherence Tomography (OCT).International journal of biomedical imaging · 2026Article
- The AROMA Dataset for Automatic Detection of Artifact Type and Severity in Retinal Optical Coherence Tomography Angiography.Ophthalmic research · 2026Article
- PDC-DETR: a Parallel Dilated Convolutional Detection Transformer for preliminary screening of multiple macular degeneration.BMC medical imaging · 2025Article
- Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.Journal of imaging informatics in medicine · 2024Article
- Deep-learning-based automated measurement of outer retinal layer thickness for use in the assessment of age-related macular degeneration, applicable to both swept-source and spectral-domain OCT imaging.Biomedical optics express · 2024Article
- Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.Bioengineering (Basel, Switzerland) · 2023Article
- Exploring Healthy Retinal Aging with Deep Learning.Ophthalmology science · 2023Article
- Deep learning network with differentiable dynamic programming for retina OCT surface segmentation.Biomedical optics express · 2023Article
- Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights.Scientific reports · 2023Article
- Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images.Journal of biomedical optics · 2023Article
- Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks.Frontiers in bioengineering and biotechnology · 2023Article
- A Multi-Task Convolutional Neural Network for Lesion Region Segmentation and Classification of Non-Small Cell Lung Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- Retinal layer segmentation in optical coherence tomography (OCT) using a 3D deep-convolutional regression network for patients with age-related macular degeneration.Biomedical optics express · 2022Article
- Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets.Journal of imaging · 2022Article
- Surgical scene generation and adversarial networks for physics-based iOCT synthesis.Biomedical optics express · 2022Article
- Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP).Translational vision science & technology · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Deep Learning-Based Retinal Nerve Fiber Layer Thickness Measurement of Murine Eyes.Translational vision science & technology · 2021Article
- CNN-based CP-OCT sensor integrated with a subretinal injector for retinal boundary tracking and injection guidance.Journal of biomedical optics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated segmentation of object boundaries or surfaces is crucial for quantitative image analysis in numerous biomedical applications. For example, retinal surfaces in optical coherence tomography (OCT) images play a vital role in the diagnosis and management of retinal diseases. Recently, graph based surface segmentation and contour modeling have been developed and optimized for various surface segmentation tasks. These methods require expertly designed, application specific transforms, including cost functions, constraints and model parameters. However, deep learning based methods are able to directly learn the model and features from training data. In this paper, we propose a convolutional neural network (CNN) based framework to segment multiple surfaces simultaneously. We demonstrate the application of the proposed method by training a single CNN to segment three retinal surfaces in two types of OCT images - normal retinas and retinas affected by intermediate age-related macular degeneration (AMD). The trained network directly infers the segmentations for each B-scan in one pass. The proposed method was validated on 50 retinal OCT volumes (3000 B-scans) including 25 normal and 25 intermediate AMD subjects. Our experiment demonstrated statistically significant improvement of segmentation accuracy compared to the optimal surface segmentation method with convex priors (OSCS) and two deep learning based UNET methods for both types of data. The average computation time for segmenting an entire OCT volume (consisting of 60 B-scans each) for the proposed method was 12.3 seconds, demonstrating low computation costs and higher performance compared to the graph based optimal surface segmentation and UNET based methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.